Sorting time is nominally similar between automated and manual sorting, but automation enables walk-away functionality. by Diane M. Wiener (1956583)

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ADT: A Generalized Algorithm and Program for Beyond Born–Oppenheimer Equations of “<i>N</i>” Dimensional Sub-Hilbert Space by Koushik Naskar (7510592)

“…For the numerical case, user can directly provide <i>ab initio</i> data (adiabatic PESs and NACTs) as input files to this software or can generate those input files through in-built python codes interfacing MOLPRO followed by ADT calculation. …”

The definition of parameters of Eq 1. by Maryam Shadi (14237349)

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Schematic showcasing main steps involved in the generation of a new daughter node <i>C</i>_<i>i</i> and subsequently a new branch <i>B</i>_<i>i</i>. by Diana C. de Oliveira (9220729)

Table 1_Applying the algorithm for Proven and young in GWAS Reveals high polygenicity for key traits in Nellore cattle.xlsx by Adebisi R. Ogunbawo (21216281)

“…</p>Methods<p>A dataset containing 304,782 Nellore cattle genotyped with 437,650 SNPs (after quality control) was used for this study. The Algorithm for Proven and Young (APY), implemented in the PREGSF90 software, was used to compute the GAPY−1 matrix using 36,000 core animals (which explained 98% of the variance in the genomic matrix). …”

(a-c) Sampling event times with an intensity function . by Gerrit Großmann (9585147)

Training accuracy/loss as a function of epochs for different datasets. by Hiam Alquran (17636906)

Comparison of the user-friendly graphical interfaces and functions of the single nucleotide polymorphism (SNP) analysis pipelines. by Dong-Jun Lee (14068673)

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Best sampling for each range of selected points (<i>n</i>_<i>S</i>). by Greta Grassmann (12418506)

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The algorithm underlying neoVM. by Tanmoy Sarkar (627505)

S1 Dataset - by Peihan Wen (6324315)

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User options and main steps of the generative algorithm. by Diana C. de Oliveira (9220729)

Cross-validated estimate of MSE error for the LASSO, Ridge and Elastic Net penalties (from top to bottom respectively) as a function of <i>log</i>λ. by Omid Chatrabgoun (17960015)

Cross-validated estimate of MSE error for the LASSO, Ridge and Elastic Net penalties (from top to bottom respectively) as a function of <i>log</i>λ. by Omid Chatrabgoun (17960015)

BOFdat: Generating biomass objective functions for genome-scale metabolic models from experimental data by Jean-Christophe Lachance (6619307)

“…BOFdat has a modular implementation that divides the BOF definition process into three independent modules defined here as steps: 1) the coefficients for major macromolecules are calculated, 2) coenzymes and inorganic ions are identified and their stoichiometric coefficients estimated, 3) the remaining species-specific metabolic biomass precursors are algorithmically extracted in an unbiased way from experimental data. …”

The details of the Scelestial algorithm. by Mohammad-Hadi Foroughmand-Araabi (6658772)

“…In step 1 the tree <i>T</i> is initialized with the minimum spanning tree of the input sequences <i>S</i>. …”

Algorithm membership function. by Mohamed Raef Smaoui (9865830)

“…<p>(Top) Input Membership Function. The algorithm classifies glucose input into 4 sets: low, medium, high, and ex_high. …”

IDRAS algorithm outline. by Ron Teichner (21492434)

Algorithm and simulation parameters. by Derek E. Moulton (7053563)

“…While time-consuming, these steps only need to be performed once and then function as look-up tables. …”

Constructing the dynamical law. by Robert Marsland (8616483)